Mosaic Data Science
Fulfilling the AI Promise

In recent years, organizations have accumulated vast amounts of data due to technological advancements and the increasing digitization of business operations. However, many companies face challenges in effectively utilizing it. In fact, a typical firm holds 1.7 petabytes of data but analyzes only 12 percent of it.

That’s where AI has proven to play a pivotal role in deriving meaningful insights through data processing, pattern recognition, and predictive analytics. According to a leading news portal, 72 percent of business leaders opt for AI adoption within three years to make better use of data.

Helping fulfil the AI promise is Mosaic Data Science.

“Even with pre-built AI tools, advanced insight generation requires fine-tuning and expertise,” says Drew Clancy, the VP of Marketing & Sales at Mosaic Data Science. “We provide the necessary data science resources to optimize and extract maximum value from AI models while maintaining data privacy. No one wants a black box.”

Through its Neural Search Engine, Mosaic is revolutionizing data retrieval and analysis using cutting-edge artificial intelligence and natural language processing.

In Every Engagement, We Tailor Our Approach To Meet Customers Where They Are On The Maturity Curve. We Articulate Our Decisions At Every Stage Of AI/Ml Development, Fostering Close Collaboration With Customers' Subject Matter Experts To Seamlessly Incorporate Essential Tribal Knowledge, Establish Common Terminology, And Capture Nuanced Data Intricacies

The company aims to enhance clients’ business processes without replacing popular language models (LLMs) such as Azure OpenAI Service.

The Neural Search Engine seamlessly integrates with clients' existing cloud infrastructure. Whether they purchase a cloud instance with AI tools or not, the tool can enhance operations without disrupting the existing setup.

Taking a Collaborative Approach

Mosaic recognizes that integrating and adopting insight engines into existing IT infrastructure is a task easier said than done. The company collaborates closely with clients throughout the integration process, guiding them seamlessly from data discovery and preparation to deploying effective approaches.

This includes determining the optimal methods for introducing new capabilities to users and providing comprehensive education to ensure successful implementation. Below are the benefits that Neural Search Engine offers:

• Time-Consuming Manual Tagging: Many organizations resort to manual data tagging, which is labor-intensive, error-prone, and costly. Neural Search Engine offers efficient tagging automation through neural networks, reducing the need for manual data tagging and saving time and resources.

• Contextual Understanding: The semantic gap between how humans express queries and how traditional search engines interpret them can lead to missed opportunities and misunderstandings. Neural Search Engine leverages Natural Language Processing (NLP) and deep learning to understand context and semantics, providing highly relevant results.

• Secure Data Handling: The Neural Search Engine rapidly prototypes LLMs, leveraging transfer learning to keep data and results secure behind your firewalls.

• Seamless Processing of Unstructured Data: Enterprise data is diverse, including structured and unstructured data, making it challenging to create unified search experiences. Not all LLMs can handle these disparate sources. Neural Search Engine excels in processing unstructured data such as text, voice, and images, bringing all data types into the search fold.
• User-Friendly: The Neural Search Engine interface is custom-built around your firm’s needs with an eye towards simplicity and user adoption. The UI is designed to garner human-in-the-loop feedback to help the AI learn continuously.

To facilitate smoother transitions and user adoption, especially for organizations with less mature analytics capabilities, Mosaic has developed certain best practices, including tailored training programs, intuitive user interfaces, and ongoing support to empower users and drive successful implementation.

“In every engagement, we tailor our approach to meet customers where they are on the maturity curve. We articulate our decisions at every stage of AI/ML development, fostering close collaboration with customers' subject matter experts to seamlessly incorporate essential tribal knowledge, establish common terminology, and capture nuanced data intricacies,” says Clancy.

Leveraging State-Of-The-Art NLP Techniques

Mosaic empowers clients to leverage AI and ML to enhance data retrieval, handle unstructured data, and elevate analysis beyond traditional search engines. Neural Search Engine's core functionalities include advanced NLP algorithms, rapid prototyping of data pipelines, tuning of large language models, and continuous learning, ensuring superior results and adaptability to evolving data landscapes.

“We leverage state-of-the-art NLP techniques,” says Juan Rebollo, Principal Data Scientist at Mosaic. The enterprise knowledge is vectorized and stored in a database, which allows us to find the most relevant content for a given user question by matching the question vector to the vectors in the database. This approach is more robust than traditional keyword matching since it encodes the semantic meaning of the text, i.e., in-context meaning.”

Once relevant content is found, the Neural Search Engine uses transformer-based question-answering models for text or tables, summarization models, or large language models to find specific answers to questions. More importantly, the tool carefully engineers the prompts put into the LLM to ensure the models provide answers using the data ingested in the search engine, not general knowledge or false information.

Helping Customers Find Answers in Real-Time

The Neural Search Engine’s interactive dashboards support real-time access to multiple data sources and data types in a centralized location, allowing decision-makers to navigate and analyze large amounts of enterprise data within seconds. Its dashboards are fully customizable, adjusting to users’ needs and data requirements.

Neural Search Engine combines traditional search with generative AI and other deep learning models while leveraging AI capabilities on specific enterprise data to search millions of documents and find answers in real-time.

Sean Osis, Senior Principal Data Scientist at Mosaic says, “We believe there is a huge opportunity here to take advantage of open-source technologies. One of the significant concerns starting to materialize with generative AI and online language models is the privacy and confidentiality of information that is being passed back and forth to the models. If an organization wants to mitigate this risk, there are many tools and technologies that can be leveraged to create a custom solution that they can own and control. Opensource frameworks like Python make it easier to stay on the cutting edge, and open-source versions of language models can be ported to on-premise environments. We believe these methods can be used to create secure, customized versions of generative AI tools.”

Comprehensive Pre-Trained Models

The field of generative AI has evolved quickly in the last few years, especially since the release of the transformer architecture by Google about six years ago—leading to a significant leap in language and vision applications.

"The Future Of Generative AI Is Going To Be Incredible. I See These Technologies Being Integrated Into Everything. Much Akin To The Transformative Impact Of The "Internet-Of-Things" On Device Connectivity And Hardware, I Anticipate The Ascent Of An "Intelligence-Of-Things" Era, Ushering In The Integration Of Generative AI Technologies Into Our Devices, Software, And Digital Experiences. It’s An Exciting Time To Be A Part Of Mosaic, As We Have A Front-row Seat To The Action, And I See Us Developing Many Of The Specific Use Cases Where This Intelligence Can Be Embedded"

With generative AI, Mosaic now has these pre-trained models that are becoming so comprehensive that they are already embedded with knowledge and understanding to tackle a wide range of use cases with little to no training.

“It’s not just language models; now, we have models with a vast knowledge of art, photographs, music, and even technical content like engineering plans,” says Osis.

The Right Blend of Technical Know-how and Healthy Scepticism

Mosaic stays ahead of the curve in the rapidly evolving field of insight engines and AI through a commitment to continuous learning and innovation. Its research and development initiatives, engagement with the broader AI community, and professional development ensure that clients benefit from the latest advancements in the field.

“The future of generative AI is going to be incredible,” says Osis. I see these technologies being integrated into everything. Much akin to the transformative impact of the "internet-of-things" on device connectivity and hardware, I anticipate the ascent of an "intelligence-of-things" era, ushering in the integration of generative AI technologies into our devices, software, and digital experiences. It’s an exciting time to be a part of Mosaic, as we have a frontrow seat to the action, and I see us developing many of the specific use cases where this intelligence can be embedded.

Mosaic has layered everything from anomaly detection on purchase orders to geological sentiment on oil reservoirs to deliver AI features that deliver immediate value. In every Neural Search Engine development engagement, the company collaborates closely with clients’ stakeholders to ensure the tool delivers as promised, going into much more depth than pre-built solutions can go.